The exemplary embodiments generally relate to marking and imaging devices, architecture and document services, and specifically relate to vector error diffusion.
There are many methods of rendering gray images on an output device. One such example is error diffusion. Error diffusion can render complex images that contain a mixture of text and picture reasonably well. The utilization of error diffusion eliminates the need to have image segmentation to separate the text from the picture so that the picture aspect of the document can be screened and the text aspect of the document can be threshold.
Related error diffusion is described in U.S. Pat. No. 5,809,177 issued Sep. 15, 1998 to Metcalfe et al. FIG. 1 is a flowchart of a typical error diffusion binarization system. In Step S1 of this process, the video signal for pixel X is modified to include the accumulated error diffused to this pixel from previous threshold processes. The modified video signal value X is compared at Step S2 with the value 128, assuming a video range between 0 and 255. If Step S2 determines that the modified video signal value X is greater than or equal to 128, the process proceeds to Step S4 wherein a value is output to indicate the tuning ON of pixel X. The process then proceeds to calculate the error associated with the threshold process at Step S6 wherein this error, Y, is calculate as being X-255.
On the other hand, if Step S2 determines that the modified video signal value X is less than 128, a signal is output at Step S3 indicating that the pixel X is to be turned OFF. The process then proceeds to Step S5 wherein the error, Y, is calculated as being equal to the value X.
The error calculated in either Steps S5 or S6 is multiplied by weighting coefficients and distributed to downstream pixels in Step S7. Thus, the error from the threshold process is diffused to adjacent pixels.
Related approaches to error diffusion include noise methods, alternating weights, and deterministic bit flipping. U.S. Pat. Nos. 5,521,989 and 5,809,177 describe adding noise to the threshold so that it can be filtered by a scalar error diffuser to make its contribution to the output pixels predominantly high frequency or blue noise. Alternating weights is described in U.S. Pat. No. 6,608,700 issued Aug. 19, 2003 to Mantell, which distributes fractional weighted portions of an error to selected subsequent pixels. Deterministic bit flipping is described in A. Magrath and M. Sandler, “A Sigma-Delta Modulator Topology with High Linearity,” IEEE International Symposium on Circuits and Systems, Jun. 9-12, 1997, Hong Kong. Deterministic Bit Flipping is applied to error diffusion in N. Damera-Venkata, IEEE Transactions of Image Processing, Vol. 10, No. 1, January 2001, and its advantages are discussed in T. Chang and J. Allebach, “Memory Efficient Error Diffusion,” IEEE Transactions on Image Processing, Vol. 12, No. 11, November 2003.